Customizable Machine Learning Models for Rapid Microplastic Identification Using Raman Microscopy
Bibliographic record
Abstract
Variations in Raman spectroscopic instrumentation alter data structure, introducing inconsistencies that disrupt the development of community-wide analytical tools. This dataset consists of Raman spectra for a variety of common plastics full-window Raman spectra that are both high resolution (<1 cm-1 wavenumber spacing) and span the full range of 100 to 4000 cm-1. The utility of this data structure for creating advanced data analysis tools is demonstrated by using the data to train several different classification models, then applying the models to classify spectra acquired on 2-dimensional Raman microscopic maps of diverse plastic microparticles. Specifically, the sklearn package in python is used to train models based on random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. This dataset provides flexibility to downgrade the spectroscopic resolution of the data such that classification models can be tailored for individual instrument setups: sample tests show that high classification accuracy is maintained even when downgrading the Raman shift spacing to 1, 2, 4, or 8 cm–1. The training data were created by the authors. The data were also tested using Raman spectra obtained from the public domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".